scholarly journals Physics-Informed Machine Learning for Predictive Turbulence Modeling: Towards a Complete Framework.

2016 ◽  
Author(s):  
Jianxun Wang ◽  
Jinlong Wu ◽  
Julia Ling ◽  
Gianluca Iaccarino ◽  
Heng Xiao
Keyword(s):  
Machine Learning ◽  
Turbulence Modeling ◽  
Complete Framework

Machine learning methods for turbulence modeling in subsonic flows around airfoils

2019 ◽  
Vol 31 (1) ◽  
pp. 015105 ◽  
Author(s):  
Linyang Zhu ◽  
Weiwei Zhang ◽  
Jiaqing Kou ◽  
Yilang Liu
Keyword(s):  
Machine Learning ◽  
Turbulence Modeling ◽  
Learning Methods ◽  
Subsonic Flows ◽  
Machine Learning Methods

Modeling the pressure strain correlation in turbulent flows using deep neural networks

2021 ◽  
pp. 095440622110429
Author(s):  
Jyoti P Panda ◽  
Hari V Warrior
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Turbulent Flows ◽  
Reynolds Stress ◽  
Channel Flow ◽  
Turbulence Modeling ◽  
Critical Role ◽  
Turbulent Channel Flow ◽  
Correlation Models ◽  
Turbulence Data

The pressure strain correlation plays a critical role in the Reynolds stress transport modeling. Accurate modeling of the pressure strain correlation leads to the proper prediction of turbulence stresses and subsequently the other terms of engineering interest. However, classical pressure strain correlation models are often unreliable for complex turbulent flows. Machine learning–based models have shown promise in turbulence modeling, but their application has been largely restricted to eddy viscosity–based models. In this article, we outline a rationale for the preferential application of machine learning and turbulence data to develop models at the level of Reynolds stress modeling. As an illustration, we develop data-driven models for the pressure strain correlation for turbulent channel flow using neural networks. The input features of the neural networks are chosen using physics-based rationale. The networks are trained with the high-resolution DNS data of turbulent channel flow at different friction Reynolds numbers (Reλ). The testing of the models is performed for unknown flow statistics at other Reλ and also for turbulent plane Couette flows. Based on the results presented in this article, the proposed machine learning framework exhibits considerable promise and may be utilized for the development of accurate Reynolds stress models for flow prediction.

scholarly journals Machine learning-augmented turbulence modeling for RANS simulations of massively separated flows

2021 ◽  
Vol 6 (6) ◽  
Author(s):  
Pedro Stefanin Volpiani ◽  
Morten Meyer ◽  
Lucas Franceschini ◽  
Julien Dandois ◽  
Florent Renac ◽  
...  
Keyword(s):  
Machine Learning ◽  
Turbulence Modeling ◽  
Separated Flows ◽  
Massively Separated Flows ◽  
Rans Simulations

scholarly journals Machine Learning for Turbulence Modeling.

2017 ◽  
Author(s):  
Julia Ling ◽  
Jeremy Templeton
Keyword(s):  
Machine Learning ◽  
Turbulence Modeling

Ethology-Based Approximate Adaptive Learning

2012 ◽  
pp. 1856-1878
Author(s):  
James F. Peters ◽  
Shabnam Shahfar
Keyword(s):  
Machine Learning ◽  
Learning Theory ◽  
Adaptive Learning ◽  
Learning Algorithm ◽  
Near Sets ◽  
Practical Applications ◽  
Q Learning ◽  
Changing Environments ◽  
Complete Framework ◽  
Observed Behavior

The problem considered in this chapter is how to use the observed behavior of organisms as a basis for machine learning. The proposed approach for machine learning combines near sets and ethology. It leads to novel forms of Q-learning algorithm that have practical applications in the controlling the behavior of machines, which learn to adapt to changing environments. Both traditional and new forms of adaptive learning theory and applications are considered in this chapter. A complete framework for an ethology-based approximate adaptive learning is established by using near sets.

Ethology-Based Approximate Adaptive Learning

2011 ◽  
pp. 469-494
Author(s):  
James F. Peters ◽  
Shabnam Shahfar
Keyword(s):  
Machine Learning ◽  
Learning Theory ◽  
Adaptive Learning ◽  
Learning Algorithm ◽  
Near Sets ◽  
Practical Applications ◽  
Q Learning ◽  
Changing Environments ◽  
Complete Framework ◽  
Observed Behavior

The problem considered in this chapter is how to use the observed behavior of organisms as a basis for machine learning. The proposed approach for machine learning combines near sets and ethology. It leads to novel forms of Q-learning algorithm that have practical applications in the controlling the behavior of machines, which learn to adapt to changing environments. Both traditional and new forms of adaptive learning theory and applications are considered in this chapter. A complete framework for an ethology-based approximate adaptive learning is established by using near sets.

The use of the Reynolds force vector in a physics informed machine learning approach for predictive turbulence modeling

2019 ◽  
Vol 192 ◽  
pp. 104258 ◽  
Author(s):  
Matheus A. Cruz ◽  
Roney L. Thompson ◽  
Luiz E.B. Sampaio ◽  
Raphael D.A. Bacchi
Keyword(s):  
Machine Learning ◽  
Turbulence Modeling ◽  
Learning Approach ◽  
Force Vector ◽  
Machine Learning Approach

scholarly journals An iterative machine-learning framework for RANS turbulence modeling

2021 ◽  
Vol 90 ◽  
pp. 108822
Author(s):  
Weishuo Liu ◽  
Jian Fang ◽  
Stefano Rolfo ◽  
Charles Moulinec ◽  
David R Emerson
Keyword(s):  
Machine Learning ◽  
Turbulence Modeling ◽  
Learning Framework

scholarly journals Machine Learning Approach

2022 ◽  
pp. 349-366
Author(s):  
Roopashree S. ◽  
Anitha J. ◽  
Madhumathy P.
Keyword(s):  
Machine Learning ◽  
Weather Conditions ◽  
Medicinal Herbs ◽  
Machine Learning Techniques ◽  
Learning Approach ◽  
Domain Experts ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Insufficient Knowledge ◽  
Complete Framework

Ayurveda medicines uses herbs for curing many ailments without side effects. The biggest concern related to Ayurveda medicine is extinction of many important medicinal herbs, which may be due to insufficient knowledge, weather conditions, and urbanization. Another reason consists of lack of online facts on Indian herbs because it is dependent on books and experts. This concern has motivated in utilizing the machine learning techniques to identify and reveal few details of Indian medicinal herbs because, until now, it is identified manually, which is cumbersome and may lead to errors. Many researchers have shown decent results in identifying and classifying plants with good accuracy and robustness. But no complete framework and strong evidence is projected on Indian medicinal herbs. Accordingly, the chapter aims to provide an outline on how machine learning techniques can be adopted to enrich the knowledge of Indian herbs, which advantages both common man and the domain experts with wide information on traditional herbs.

scholarly journals Machine learning accelerated turbulence modeling of transient flashing jets

2021 ◽  
Vol 33 (12) ◽  
pp. 127104
Author(s):  
David Schmidt ◽  
Romit Maulik ◽  
Konstantinos Lyras
Keyword(s):  
Machine Learning ◽  
Turbulence Modeling
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